Packages

final class Task extends GeneratedMessageV3 with TaskOrBuilder

Describes a single task in a model and all its properties.
A task corresponds to a single output of the model.
Multiple tasks in the same problem statement correspond to different outputs
of the model.

Protobuf type tensorflow.metadata.v0.Task

Source
Task.java
Linear Supertypes
TaskOrBuilder, GeneratedMessageV3, Serializable, AbstractMessage, Message, MessageOrBuilder, AbstractMessageLite[MessageType, BuilderType], MessageLite, MessageLiteOrBuilder, AnyRef, Any
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Inherited
  1. Task
  2. TaskOrBuilder
  3. GeneratedMessageV3
  4. Serializable
  5. AbstractMessage
  6. Message
  7. MessageOrBuilder
  8. AbstractMessageLite
  9. MessageLite
  10. MessageLiteOrBuilder
  11. AnyRef
  12. Any
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Visibility
  1. Public
  2. Protected

Value Members

  1. def equals(obj: AnyRef): Boolean
    Definition Classes
    Task → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  2. def findInitializationErrors(): List[String]
    Definition Classes
    AbstractMessage → MessageOrBuilder
  3. def getAllFields(): Map[FieldDescriptor, AnyRef]
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  4. def getDefaultInstanceForType(): Task
    Definition Classes
    Task → MessageOrBuilder → MessageLiteOrBuilder
    Annotations
    @Override()
  5. def getDescriptorForType(): Descriptor
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  6. def getField(field: FieldDescriptor): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  7. def getInitializationErrorString(): String
    Definition Classes
    AbstractMessage → MessageOrBuilder
  8. def getIsAuxiliary(): Boolean

    True to indicate the task is an auxiliary task in a multi-task setting.
Auxiliary tasks are of minor relevance for the application and they are
added only to improve the performance on a primary task (by providing
additional regularization or data augmentation), and thus are not
considered in the meta optimization process (but may be utilized in the
learner optimization).


    True to indicate the task is an auxiliary task in a multi-task setting.
Auxiliary tasks are of minor relevance for the application and they are
added only to improve the performance on a primary task (by providing
additional regularization or data augmentation), and thus are not
considered in the meta optimization process (but may be utilized in the
learner optimization).

    bool is_auxiliary = 6;

    returns

    The isAuxiliary.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  9. def getName(): String

    The task name. Tasks within the same ProblemStatement should have unique
names. This a REQUIRED field in case of multi-task learning problems.


    The task name. Tasks within the same ProblemStatement should have unique
names. This a REQUIRED field in case of multi-task learning problems.

    string name = 5;

    returns

    The name.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  10. def getNameBytes(): ByteString

    The task name. Tasks within the same ProblemStatement should have unique
names. This a REQUIRED field in case of multi-task learning problems.


    The task name. Tasks within the same ProblemStatement should have unique
names. This a REQUIRED field in case of multi-task learning problems.

    string name = 5;

    returns

    The bytes for name.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  11. def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  12. def getParserForType(): Parser[Task]
    Definition Classes
    Task → GeneratedMessageV3 → Message → MessageLite
    Annotations
    @Override()
  13. def getPerformanceMetric(index: Int): PerformanceMetric

    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.


    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.

    repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  14. def getPerformanceMetricCount(): Int

    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.


    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.

    repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  15. def getPerformanceMetricList(): List[PerformanceMetric]

    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.


    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.

    repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  16. def getPerformanceMetricOrBuilder(index: Int): PerformanceMetricOrBuilder

    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.


    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.

    repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  17. def getPerformanceMetricOrBuilderList(): List[_ <: PerformanceMetricOrBuilder]

    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.


    This field includes performance metrics of this head that are important to
the problem owner and need to be monitored and reported. However, unlike
fields such as "meta_optimization_target", these metrics are not
not automatically used in meta-optimization.

    repeated .tensorflow.metadata.v0.PerformanceMetric performance_metric = 4;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  18. def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  19. def getRepeatedFieldCount(field: FieldDescriptor): Int
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  20. def getSerializedSize(): Int
    Definition Classes
    Task → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  21. def getTaskWeight(): Double

    If a Problem is composed of multiple sub-tasks, the weight of each task
determines the importance of solving each sub-task. It is used to
rank and select the best solution for multi-task problems.
Not meaningful for a problem with one task.
If the problem has multiple tasks and all task_weight=0 (unset) then all
tasks are weighted equally.


    If a Problem is composed of multiple sub-tasks, the weight of each task
determines the importance of solving each sub-task. It is used to
rank and select the best solution for multi-task problems.
Not meaningful for a problem with one task.
If the problem has multiple tasks and all task_weight=0 (unset) then all
tasks are weighted equally.

    double task_weight = 2;

    returns

    The taskWeight.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  22. def getType(): Type

    Specification of the label and weight columns, and the type of the
prediction or classification.


    Specification of the label and weight columns, and the type of the
prediction or classification.

    .tensorflow.metadata.v0.Type type = 1;

    returns

    The type.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  23. def getTypeOrBuilder(): TypeOrBuilder

    Specification of the label and weight columns, and the type of the
prediction or classification.


    Specification of the label and weight columns, and the type of the
prediction or classification.

    .tensorflow.metadata.v0.Type type = 1;

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  24. def getUnknownFields(): UnknownFieldSet
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  25. def hasField(field: FieldDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → MessageOrBuilder
  26. def hasOneof(oneof: OneofDescriptor): Boolean
    Definition Classes
    GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
  27. def hasType(): Boolean

    Specification of the label and weight columns, and the type of the
prediction or classification.


    Specification of the label and weight columns, and the type of the
prediction or classification.

    .tensorflow.metadata.v0.Type type = 1;

    returns

    Whether the type field is set.

    Definition Classes
    Task → TaskOrBuilder
    Annotations
    @Override()
  28. def hashCode(): Int
    Definition Classes
    Task → AbstractMessage → Message → AnyRef → Any
    Annotations
    @Override()
  29. final def isInitialized(): Boolean
    Definition Classes
    Task → GeneratedMessageV3 → AbstractMessage → MessageLiteOrBuilder
    Annotations
    @Override()
  30. def newBuilderForType(): Builder
    Definition Classes
    Task → Message → MessageLite
    Annotations
    @Override()
  31. def toBuilder(): Builder
    Definition Classes
    Task → Message → MessageLite
    Annotations
    @Override()
  32. def toByteArray(): Array[Byte]
    Definition Classes
    AbstractMessageLite → MessageLite
  33. def toByteString(): ByteString
    Definition Classes
    AbstractMessageLite → MessageLite
  34. final def toString(): String
    Definition Classes
    AbstractMessage → Message → AnyRef → Any
  35. def writeDelimitedTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])
  36. def writeTo(output: CodedOutputStream): Unit
    Definition Classes
    Task → GeneratedMessageV3 → AbstractMessage → MessageLite
    Annotations
    @Override()
  37. def writeTo(output: OutputStream): Unit
    Definition Classes
    AbstractMessageLite → MessageLite
    Annotations
    @throws(classOf[java.io.IOException])